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new 13b514b WIP.
13b514b is described below
commit 13b514b18c483921878e586e767dff0951688310
Author: Aaron Radzinski <[email protected]>
AuthorDate: Sat Nov 14 21:48:02 2020 -0800
WIP.
---
_data/news.yml | 4 +-
blogs/short_term_memory.html | 180 ++++++++++++++++++++++++++++++++++++++++++-
images/stm1.png | Bin 0 -> 50047 bytes
tools/embedded_probe.html | 8 +-
4 files changed, 185 insertions(+), 7 deletions(-)
diff --git a/_data/news.yml b/_data/news.yml
index 5c8aefa..8d9da28 100644
--- a/_data/news.yml
+++ b/_data/news.yml
@@ -17,7 +17,7 @@
- title: π ΠΡΠ°ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΡ Apache NlpCraft
url: https://habr.com/ru/post/526950/
- excerpt: Π¦Π΅Π»Ρ ΠΏΡΠΎΠ΅ΠΊΡΠ° β ΡΠΎΡΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠΏΡΠΎΡΠ΅Π½ΠΈΠ΅ Π΄ΠΎΡΡΡΠΏΠ° ΠΊ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌ NLP
ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ. Π£Π»ΠΎΠ²ΠΈΡΡ Π±Π°Π»Π°Π½Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΡΠΎΡΡΠΎΡΠΎΠΉ Π²Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π² NLP
ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΡ ΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΎΠΉ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΠΉ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ.
+ excerpt: Π¦Π΅Π»Ρ ΠΏΡΠΎΠ΅ΠΊΡΠ° - ΡΠΎΡΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠΏΡΠΎΡΠ΅Π½ΠΈΠ΅ Π΄ΠΎΡΡΡΠΏΠ° ΠΊ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌ NLP
ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ. Π£Π»ΠΎΠ²ΠΈΡΡ Π±Π°Π»Π°Π½Ρ ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΡΠΎΡΡΠΎΡΠΎΠΉ Π²Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π² NLP
ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΡ ΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΎΠΉ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΠΉ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ.
author: Π‘Π΅ΡΠ³Π΅ΠΉ ΠΠ°ΠΌΠΎΠ²
publish_date: November 8, 2020
avatar_url: images/sergey_kamov_avatar.png
@@ -49,7 +49,7 @@
- title: π Short-Term Memory - Maintaining Conversation Context
url: /blogs/short_term_memory.html
- excerpt: Short-Term Memory (STM), a technique used to maintain
conversational context in NLPCraft. Maintaining the proper conversation context
β remembering what the current conversation is about β is essential for all
human interaction and thus essential for computer-based natural language
understanding.
+ excerpt: Short-Term Memory (STM), a technique used to maintain
conversational context in NLPCraft. Maintaining the proper conversation context
- remembering what the current conversation is about - is essential for all
human interaction and thus essential for computer-based natural language
understanding.
author: Aaron Radzinksi
avatar_url: images/lion.jpg
publish_date: July 26, 2019
diff --git a/blogs/short_term_memory.html b/blogs/short_term_memory.html
index 12b20fa..ce09d25 100644
--- a/blogs/short_term_memory.html
+++ b/blogs/short_term_memory.html
@@ -28,7 +28,185 @@ publish_date: July 26, 2019
-->
<section id="">
-
+ <p>
+ <img alt="" class="img-fluid" src="/images/stm1.png">
+ </p>
+ <p>
+ In this blog, Iβll try to give a high-level overview of STM -
Short-Term Memory, a technique used to
+ maintain conversational context in NLPCraft. Maintaining the proper
conversation context - remembering
+ what the current conversation is about - is essential for all human
interaction and thus essential for
+ computer-based natural language understanding. To my knowledge,
NLPCraft provides one of the most advanced
+ implementations of STM, especially considering how tightly it is
integrated with NLPCraftβs unique
+ intent-based matching (Googleβs <a target=google
href="https://cloud.google.com/dialogflow/";>DialogFlow</a> is very similar yet).
+ </p>
+ <p>
+ Letβs dive in.
+ </p>
+</section>
+<section>
+ <h2 class="section-title">Parsing User Input</h2>
+ <p>
+ One of the key objectives when parsing user input sentence for Natural
Language Understanding (NLU) is to
+ detect all possible semantic entities, a.k.a <em>named entities</em>.
Letβs consider a few examples:
+ </p>
+ <ul>
+ <li>
+ <code>"Whatβs the current weather in Tokyo?"</code><br/>
+ This sentence is fully sufficient for the processing
+ since it contains the topic <code>weather</code> as well as all
necessary parameters
+ like time (<code>current</code>) and location (<code>Tokyo</code>).
+ </li>
+ <li>
+ <code>"What about Tokyo?"</code><br/>
+ This is an unclear sentence since it does not have the subject of
the
+ question - what is it about Tokyo?
+ </li>
+ <li>
+ <code>"Whatβs the weather?"</code><br/>
+ This is also unclear since we are missing important parameters
+ of location and time for our request.
+ </li>
+ </ul>
+ <p>
+ Sometimes we can use default values like the current userβs location
and the current time (if they are missing).
+ However, this can easily lead to the wrong interpretation if the
conversation has an existing context.
+ </p>
+ <p>
+ In real life, as well as in NLP-based systems, we always try to start
a conversation with a fully defined
+ sentence since without a context the missing information cannot be
obtained and the sentenced cannot be interpreted.
+ </p>
+</section>
+<section>
+ <h2 class="section-title">Semantic Entities</h2>
+ <p>
+ Letβs take a closer look at the named entities from the above examples:
+ </p>
+ <ul>
+ <li>
+ <code>weather</code> - this is an indicator of the subject of the
conversation. Note that it indicates
+ the type of question rather than being an entity with multiple
possible values.
+ </li>
+ <li>
+ <code>current</code> - this is an entity of type <code>Date</code>
with the value of <code>now</code>.
+ </li>
+ <li>
+ <code>Tokyo</code> - this is an entity of type
<code>Location</code> with two values:
+ <ul>
+ <li><code>city</code> - type of the location.</li>
+ <li><code>Tokyo, Japan</code> - normalized name of the
location.</li>
+ </ul>
+ </li>
+ </ul>
+ <p>
+ We have two distinct classes of entities:
+ </p>
+ <ul>
+ <li>
+ Entities that have no values and only act as indicators or types.
The entity <code>weather</code> is the
+ type indicator for the subject of the user input.
+ </li>
+ <li>
+ Entities that additionally have one or more specific values like
<code>current</code> and <code>Tokyo</code> entities.
+ </li>
+ </ul>
+ <div class="bq success">
+ <div style="display: inline-block; margin-bottom: 20px">
+ <a style="margin-right: 10px" target="opennlp"
href="https://opennlp.apache.org";><img src="/images/opennlp-logo.png"
height="32px" alt=""></a>
+ <a style="margin-right: 10px" target="google"
href="https://cloud.google.com/natural-language/";><img
src="/images/google-cloud-logo-small.png" height="32px" alt=""></a>
+ <a style="margin-right: 10px" target="stanford"
href="https://stanfordnlp.github.io/CoreNLP";><img
src="/images/corenlp-logo.gif" height="48px" alt=""></a>
+ <a style="margin-right: 10px" target="spacy"
href="https://spacy.io";><img src="/images/spacy-logo.png" height="32px"
alt=""></a>
+ </div>
+ <p>
+ Note that NLPCraft provides <a
href="/integrations.html">support</a> for wide variety of named entities (with
all built-in ones being properly normalized)
+ including <a href="/integrations.html">integrations</a> with
+ <a target="spacy" href="https://spacy.io/";>spaCy</a>,
+ <a target="stanford"
href="https://stanfordnlp.github.io/CoreNLP";>Stanford CoreNLP</a>,
+ <a target="opennlp" href="https://opennlp.apache.org/";>OpenNLP</a>
and
+ <a target="google"
href="https://cloud.google.com/natural-language/";>Google Natural Language</a>.
+ </p>
+ </div>
+</section>
+<section>
+ <h2 class="section-title">Incomplete Sentences</h2>
+ <p>
+ Assuming previously asked questions about the weather in Tokyo (in the
span of the ongoing conversation) one
+ could presumably ask the following questions using a <em>shorter,
incomplete</em>, form:
+ </p>
+ <ul>
+ <li>
+ <code>"What about Kyoto?</code><br/>
+ This question is missing both the subject and the time. However, we
+ can safely assume we are still talking about current weather.
+ </li>
+ <li>
+ <code>"What about tomorrow?"</code><br/>
+ Just like above we automatically assume the weather subject but
+ use <code>Kyoto</code> as the location since it was mentioned the
last.
+ </li>
+ </ul>
+ <p>
+ These are incomplete sentences. This type of short-hands cannot be
interpreted without prior context (neither
+ by humans or by machines) since by themselves they are missing
necessary information.
+ In the context of the conversation, however, these incomplete
sentences work. We can simply provide one or two
+ entities and rely on the <em>"listener"</em> to recall the rest of
missing information from a
+ <em>short-term memory</em>, a.k.a conversation context.
+ </p>
+ <p>
+ In NLPCraft, the intent-matching logic will automatically try to find
missing information in the
+ conversation context (that is automatically maintained). Moreover, it
will properly treat such recalled
+ information during weighted intent matching since it naturally has
less "weight" than something that was
+ found explicitly in the user input.
+ </p>
+</section>
+<section>
+ <h2 class="section-title">Short-Term Memory</h2>
+ <p>
+ The short-term memory is exactly that... a memory that keeps only
small amount of recently used information
+ and that evicts its contents after a short period of inactivity.
+ </p>
+ <p>
+ Letβs look at the example from a real life. If you would call your
friend in a couple of hours asking <code>"What about a day after?"</code>
+ (still talking about weather in Kyoto) - heβll likely be thoroughly
confused. The conversation is timed out, and
+ your friend has lost (forgotten) its context. You will have to explain
again to your confused friend what is that you are asking about...
+ </p>
+ <p>
+ NLPCraft has a simple rule that 5 minutes pause in conversation leads
to the conversation context reset. However,
+ what happens if we switch the topic before this timeout elapsed?
+ </p>
+</section>
+<section>
+ <h2 class="section-title">Context Switch</h2>
+ <p>
+ Resetting the context by the timeout is, obviously, not a hard thing
to do. What can be trickier is to detect
+ when conversation topic is switched and the previous context needs to
be forgotten to avoid very
+ confusing interpretation errors. It is uncanny how humans can detect
such switch with seemingly no effort, and yet
+ automating this task by the computer is anything but effortless...
+ </p>
+ <p>
+ Letβs continue our weather-related conversation. All of a sudden, we
ask about something completely different:
+ </p>
+ <ul>
+ <li>
+ <code>"How much mocha latter at Starbucks?"</code><br/>
+ At this point we should forget all about previous conversation
about weather and assume going forward
+ that we are talking about coffee in Starbucks.
+ </li>
+ <li>
+ <code>"What about Peetβs?"</code><br/>
+ We are talking about latter at Peetβs.
+ </li>
+ <li>
+ <code>"...and croissant?"</code><br/>
+ Asking about Peetβs crescent-shaped fresh rolls.
+ </li>
+ </ul>
+ <p>
+ Despite somewhat obvious logic the implementation of context switch is
not an exact science. Sometimes, you
+ can have a "soft" context switch where you donβt change the topic of
the conversation 100% but yet sufficiently
+ enough to forget at least some parts of the previously collected
context. NLPCraft has a built-in algorithm
+ to detect the hard switch in the conversation. It also exposes API to
perform a selective reset on the
+ conversation in case of βsoftβ switch.
+ </p>
</section>
diff --git a/images/stm1.png b/images/stm1.png
new file mode 100644
index 0000000..73bafbf
Binary files /dev/null and b/images/stm1.png differ
diff --git a/tools/embedded_probe.html b/tools/embedded_probe.html
index 0be9f61..1bcfa4b 100644
--- a/tools/embedded_probe.html
+++ b/tools/embedded_probe.html
@@ -83,11 +83,11 @@ public class AlarmTest {
@BeforeEach
void setUp() throws NCException, IOException {
- NCEmbeddedProbe.start(AlarmModel.class);
+ if (NCEmbeddedProbe.start(AlarmModel.class)) {
+ cli = new NCTestClientBuilder().newBuilder().build();
- cli = new NCTestClientBuilder().newBuilder().build();
-
- cli.open("nlpcraft.alarm.ex");
+ cli.open("nlpcraft.alarm.ex");
+ }
}
@AfterEach
